Abstract
Iris image analysis studies the relationship between human health and changes in the anatomy of the iris. One of the changes related to the anatomy of the iris is diabetes. This illness can be determined from the iris of human eyes because it affects the eyes. Latest advanced technologies are introduced in the image processing that helps automate the detection of diabetes based on the analysis of iris feature extractions. Various features are detected on iris such as texture, colour, histogram and shape. In this paper, the dataset of iris image from Warsaw Biobase are used to detect and recognise the rubeosis iridis by extracting their details using image processing methods. The results obtained from the experiment show that the normal and abnormal iris image can be classified using original and small size of iris image. Through this experiment, it was discovered that images for abnormal original are greater than 1,200,000 pixels while for small size are less than 35,000 pixels. On the contrary, normal original size are less than 1,200,000 pixels and for small are less than 25,000 pixel. By considering these results, the proposed method can be extended to the iris monitoring system.
Keywords
- Features extraction
- Blood vessels
- Diabetic
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wong TY et al (2018) Guidelines on diabetic eye care. Ophthalmology 125(10):1608–1622
Bhatia SK, Atole P, Kamble S, Telang P (2015) Methodology for detecting diabetic presence from iris image analysis. Int J Adv Res Comput Eng & Technol (IJARCET) 4(3)
Hussein SE, Hassan OA, Granat MH (2013) Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks. Biomed Signal Process Control 8:534–541
More SB, Pergad ND (2012) On a methodology for detecting diabetic presence from iris image analysis. In: International Conference on Power, Signals, Controls and Computation 2012 Jan 3, IEEE, pp 1–6
Walvekar M, Salunke G (2015) Detection of diabetic retinopathy with feature extraction using image processing. Int J Emerg Technol Adv Eng 5(1):133–137
Banzi JF, Xue Z (2015) An automated tool for non-contact, real time early detection of diabetes by computer vision. Int J Mach Learn Comput 5(3):225
Xu G, Zhang Z, Ma Y (2008) An image segmentation based method for iris feature extraction. J China Univ Posts Telecommun 15(1):96–117
Abidin ZZ, Manaf M, Shibghatullah AS, Anawar S, Ahmad R (2013) Feature extraction from epigenetic traits using edge detection in iris recognition system. In: IEEE International Conference on Signal and Image Processing Applications, pp 145–149
Samant P, Agarwal R (2017) Diagnosis of diabetes using computer methods: soft computing methods for diabetes detection using iris. Threshold 8:9
Lipton P, Chaturvedi M (2016) Improve the performance of iris recognition using genetic algorithm. Int J Innovations Eng Technol (IJIET) 7(1):296–304
BioBase-Disease-Iris v1.0 is publicly available for research and non-commercial use. See https://zbum.ia.pw.edu.pl/EN/node/46
BioBase-Disease-Iris v2.1 is publicly available for research and non-commercial use. See https://zbum.ia.pw.edu.pl/EN/node/46
Acknowledgements
This research work is suported by research grant funded by Universiti Malaysia Pahang (RDU1703233). The authors also would like to thank the Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang for financial support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Karim, R.A., Mobin, N.A.A.A., Arshad, N.W., Zakaria, N.F., Bakar, M.Z.A. (2020). Early Rubeosis Iridis Detection Using Feature Extraction Process. In: Kasruddin Nasir, A.N., et al. InECCE2019. Lecture Notes in Electrical Engineering, vol 632. Springer, Singapore. https://doi.org/10.1007/978-981-15-2317-5_32
Download citation
DOI: https://doi.org/10.1007/978-981-15-2317-5_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2316-8
Online ISBN: 978-981-15-2317-5
eBook Packages: EngineeringEngineering (R0)